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Title: Robust object tracking algorithm for autonomous vehicles in complex scenes
Authors: Cao, Jingwei
Song, Chuanxue
Song, Shixin
Xiao, Feng
Zhang, Xu
Liu, Zhiyang 
Ang, Marcelo H., Jr. 
Keywords: Autonomous vehicles
Deep learning
Environmental perception
Object tracking
Siamese network
Issue Date: 14-Aug-2021
Publisher: MDPI AG
Citation: Cao, Jingwei, Song, Chuanxue, Song, Shixin, Xiao, Feng, Zhang, Xu, Liu, Zhiyang, Ang, Marcelo H., Jr. (2021-08-14). Robust object tracking algorithm for autonomous vehicles in complex scenes. Remote Sensing 13 (16) : 3234. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: Object tracking is an essential aspect of environmental perception technology for autonomous vehicles. The existing object tracking algorithms can only be applied well to simple scenes. When the scenes become complex, the algorithms have poor tracking performance and in-sufficient robustness, and the problems of tracking drift and object loss are prone to occur. Therefore, a robust object tracking algorithm for autonomous vehicles in complex scenes is pro-posed. Firstly, we study the Siam-FC network and related algorithms, and analyze the problems that need to be addressed in object tracking. Secondly, the construction of a double-template Siamese network model based on multi-feature fusion is described, as is the use of the improved MobileNet V2 as the feature extraction backbone network, and the attention mechanism and template online update mechanism are introduced. Finally, relevant experiments were carried out based on public datasets and actual driving videos, with the aim of fully testing the tracking performance of the proposed algorithm on different objects in a variety of complex scenes. The results showed that, compared with other algorithms, the proposed algorithm had high tracking accuracy and speed, demonstrated stronger robustness and anti-interference abilities, and could still accurately track the object in real time without the introduction of complex structures. This algorithm can be effectively applied in intelligent vehicle driving assistance, and it will help to promote the further development and improvement of computer vision technology in the field of environmental perception. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Source Title: Remote Sensing
ISSN: 2072-4292
DOI: 10.3390/rs13163234
Rights: Attribution 4.0 International
Appears in Collections:Staff Publications

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